历史的回响：技术革命是财富增长的真正引擎 但斌从1998年君安研究所的岁月谈起，以亲身经历串联起中国资本市场的变迁。他指出，过去数十年间，电子计算机、互联网、移动互联网三大技术浪潮催 生了无数投资机遇。尽管期间经历金融危机、高通胀等干扰，但推动社会前进的根本动力始终是技术进步。 "如果我们回到2004年东方港湾成立时，能预见移动互联网时代，就应该将全部资金换成港币买入腾讯。"但斌以21年六七百倍回报的案例，道出时代机遇的 惊人能量。而今面对2022年开启的人工智能革命，他直言东方港湾做出了与当年类似的战略抉择——重仓以英伟达为代表的AI领军企业。 全球视野：选择比努力更重要 专题：2025分析师大会：资本市场"奥斯卡"启幕 炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 11月28日，2025分析师大会暨第七届新浪财经"金麒麟"最佳分析师颁奖盛典隆重召开。东方港湾董事长但斌主旨演讲。但斌以一场充满历史纵深感的演讲， 向在场年轻分析师们传递了一个核心信念：我们正站在一个堪比工业革命的智能时代开端，投资生涯中最大的遗憾，莫过于辜负这样的伟大时代。 针对当前市场对AI泡沫的担忧，但斌引用历 ...

Core Insights - The AI industry is entering a rapid change cycle, with 2025 being a pivotal year for the development of large models, particularly with the emergence of DeepSeek, which is reshaping the global landscape and promoting open-source initiatives [1][10][18] - The dual-core driving force of AI development is characterized by the United States and China, each following distinct paths, with key technologies accelerating towards engineering applications [1][10][11] - Despite advancements in model capabilities, challenges in real-world application remain prevalent, indicating a shift in focus from "large models" to "AI+" [1][10][19] Group 1: Global Large Model Landscape - The global large model development is driven by a dual-core approach, with the U.S. leading in closed-source models and China focusing on open-source models [10][11][13] - OpenAI, Anthropic, and Google represent the leading trio in the large model arena, each adopting differentiated strategic paths [17] - DeepSeek's emergence marks a significant breakthrough for China's large model development, showcasing the potential of open-source models [18][19] Group 2: Key Technological Evolution - The evolution of large models is marked by four major technological trends: native multimodal integration, reasoning capabilities, long context memory, and agentic AI [22][24] - Native multimodal architectures are replacing text-centric models, allowing for seamless integration of various modalities [23] - Reasoning capabilities are becoming a core feature of advanced models, enabling them to demonstrate their thought processes [24][26] Group 3: Industry Chain and Infrastructure - The AI infrastructure is still dominated by Nvidia, with a slow transition towards a multi-polar ecosystem despite the emergence of alternatives like Google’s TPU and AMD’s chips [47][48] - The AI industry is shifting from reliance on a few cloud providers to a more collaborative funding model, with Nvidia and OpenAI acting as dual cores driving the ecosystem [51][52] Group 4: Application Layer Opportunities - Large model companies are positioning themselves as "super assistants" while also aiming to control user entry points through various products and services [53][54] - Independent application companies can find opportunities in vertical markets that require deep industry understanding and complex workflow integration [55][56] - The evolution of AI applications is moving towards intelligent agents capable of autonomous operation, indicating a significant shift in application development paradigms [61][62]

从人力资源到会计行业，越来越多非技术从业者开始使用 ChatGPT、Gemini、Claude、Replit 等工具自己制作应用。皮查伊认为，氛围编程让使用者能直接 把想法做成模型，而不仅仅是描述。"过去只能用语言解释，现在可以先做出一点雏形再展示。" IT之家 11 月 28 日消息，据《商业内幕》今日报道，谷歌 CEO 桑达尔・皮查伊认为，氛围编程（Vibe coding）正在为非技术背景的工作者打开同样的大 门，就像博客曾让普通写作者找到职业入口，YouTube 也让更多人把内容创作变成事业一样。 这种方式甚至开始改变科技公司内部的流程。Meta 的产品经理已经用氛围编程做原型并展示给扎克伯格。皮查伊透露，谷歌内部首次提交代码变更的员工 数量也明显增加。 皮查伊在一档谷歌开发者播客中表示，氛围编程让创作应用和网站变得更加愉快，人们即便没有编码基础，也能随时尝试搭建原型。技术正在变得更易接 近，更有趣，而且还会不断进化，从而让人再度兴奋起来。 不过，随着氛围编程的快速普及，把编码工作交给 AI 也带来风险。IT之家从报道中获悉，皮查伊强调，大型关键系统仍必须由专业工程师负责，因为安全 与准确性都无法被忽视 ...

这一动向也被外界认为与华为鸿蒙在生态上的思路类似，同时引发了关于 Google 过去 Fuchsia 系统的回忆与讨论。 欲将 Android 放到 PC 端，这并非是 Google 的第一次尝试 转自：CSDN（ID：CSDNnews） 据外媒 Android Authority 报道，Google 正在打造一款全新的桌面操作系统，代号为 Aluminium OS。此系统的目的是将 Android 和 ChromeOS 整合到一个桌面平台上，这意味着 Google 有意逐步用 Android 取代 ChromeOS，将自己的桌面产品策略重新洗牌。 说起来，Android 在手机、平板、电视、车载系统乃至 XR 设备中无处不在，但在传统笔记本电脑或台式机上的影响力却几乎为零，毕竟那是 ChromeOS 的天下。 然而，从市场份额来看，ChromeOS 远不及 Windows 和 macOS，甚至排在了 Linux 的后面。 不过，这次失败并未让 Google 放弃"一套系统连接所有硬件"的想法。不同的是，这一次它采取了更为稳妥的策略，选择逐步推进。 一年前，外媒称，Google 希望 Android 成为统一 ...

#分享 Google Gemini Enterprise开放商务版30天免费试用，有反馈称用个人邮箱也可申请试用。对Gemini 3有需求的可以试试。🚪传送门https://t.co/1j0xl6Fp07 https://t.co/dZqNdlAlAm ...

Core Insights - This year's Thanksgiving was notably influenced by AI-generated images, overshadowing traditional family gatherings and creating a new form of digital memory [1][10][21] Group 1: AI Technology Impact - Google recently launched Nano Banana Pro, which significantly enhances the realism of AI-generated images, making them indistinguishable from real photographs [10][12] - The AI-generated images feature meticulously crafted details, such as tablecloth textures and lighting effects, which contribute to their believability [12][14] - The combination of "celebrities + holidays + food" in AI images has led to increased acceptance and sharing among users [14] Group 2: Social Media Dynamics - Social media platforms favor visually engaging content, leading to the rapid spread of these AI images, which are perceived as humorous and light-hearted [19] - Users are aware that the images are fake yet still engage with them, driven by a desire for shared laughter and emotional connection during the holiday season [15][19] - The phenomenon reflects a shift in how memories are formed, as repeated exposure to AI-generated images can replace genuine experiences in users' minds [19][21] Group 3: Cultural Reflection - The AI images represent an idealized version of Thanksgiving, contrasting sharply with the chaotic reality of family gatherings [17][21] - The allure of these perfect images serves as a reminder of the importance of real human connections over digital representations [21]

AlphaFold的下一步是与大模型融合。 不过具体方法并没有透露，或许已有所思路，甚至已经在进程之中。 五年期间，AlphaFold已经帮助全球300多万研究人员，预测了数亿种蛋白质的三维结构，并影响了超50万篇相关论文。 可以说，这是继量子力学和分子生物学革命后，生命科学的又一次重大跃迁。 正值AlphaFold问世五周年，其设计者、也是凭借AlphaFold获得诺贝尔化学奖的John Jumper公开表示： 继最初的"结构预测革命"、随后的"科研常规工具"化，AlphaFold及其继承技术正在进入新的大模型阶段。 AlphaFold+大模型 即使是在AI浪潮不断涌来的今天，AlphaFold仍然是AI+生命科学最具里程碑意义的一次落地。 作为一款由谷歌DeepMind开发的AI科研工具，AlphaFold能够精确预测蛋白质的三维结构。 利用存储在序列和结构数据库中的大量实验数据，该网络被训练以发现氨基酸序列之间的关联和模式。 于是他们利用AlphaFold先对其氨基酸序列进行结构预测，再将生成的模型拟合到密度图中，并逐步优化，直到与实验数据对齐。 最终揭示了ApoB100的笼状结构，为后续推动心血管疾病 ...

Core Insights - Google is overcoming the "innovator's dilemma" with Gemini 3 and Nano Banana Pro, leveraging its TPU computing cluster as a significant competitive advantage in the AI era [1][3] - The market underestimates the destructive impact of "inference costs" on AI business models, with Google holding pricing power due to its self-developed TPU, contrasting with competitors reliant on NVIDIA [2][4] - Gemini 3 is transforming search from a "link-finding" tool to a "decision engine," potentially increasing ad conversion rates and supporting higher ad prices [1][12] TPU and Inference Arbitrage - TPU is a critical asset for Google, designed specifically for neural network computations, providing a significant performance advantage over NVIDIA's GPUs [4][5] - Google's TPU v7 has improved performance per watt by 100% compared to its predecessor, and its inference performance is four times better than NVIDIA's H100 [5][6] - This positions Google to maintain over 50% gross margins while competitors face reduced margins due to high NVIDIA costs [6] Gemini 3 and Nano Banana Pro - Gemini 3 showcases Google's ability to convert talent into superior product capabilities, outperforming competitors like GPT-5.1 [7] - The model's native multimodal capabilities allow it to process complex data and perform tasks across various platforms, enhancing its utility [7][10] - Nano Banana Pro aims to optimize AI for mobile devices, further expanding Google's reach [7][8] Distribution and Market Position - Google benefits from a vast distribution network through Android and Chrome, allowing for zero marginal cost updates to billions of users [10][11] - The company's strategic moves, including stock buybacks, enhance shareholder value and position it favorably in the tech market [11] Business Model Evolution - Concerns about AI killing search are mitigated by the potential for AI to enhance ad targeting and conversion rates, shifting from traditional traffic distribution to high-value decision-making [12][16] - Gemini-driven search experiences are expected to yield higher ad values by providing structured comparisons rather than simple links [16][17] Conclusion - Google is uniquely positioned in the AI landscape with its "full-stack sovereignty," combining hardware, software, and user access [17][18] - The recent stock price surge reflects market recognition of Google's status as a leader in AI infrastructure, paving the way for potential future valuation increases [17][19]

鹭羽 发自 凹非寺 量子位 | 公众号 QbitAI 正值 AlphaFold 问世五周年，其设计者、也是凭借AlphaFold获得诺贝尔化学奖的 John Jumper 公开表示： AlphaFold的下一步是与大模型融合。 不过具体方法并没有透露，或许已有所思路，甚至已经在进程之中。 五年期间，AlphaFold已经帮助全球 300多万 研究人员，预测了数亿种蛋白质的三维结构，并影响了超 50万篇 相关论文。 可以说，这是继量子力学和分子生物学革命后，生命科学的又一次重大跃迁。 继最初的 "结构预测革命" 、随后的 "科研常规工具" 化，AlphaFold及其继承技术正在进入新的 大模型 阶段。 AlphaFold+大模型 现在AlphaFold已经从最初单纯地蛋白质结构预测，发展到能够处理更为复杂的多分子复合体以及更广范围的生物分子交互。 科学家们也据此，实现了相当多的成果突破： 即使是在AI浪潮不断涌来的今天，AlphaFold仍然是 AI+生命科学 最具里程碑意义的一次落地。 作为一款由 谷歌DeepMind 开发的AI科研工具，AlphaFold能够精确预测蛋白质的三维结构。 例如最近来自密苏里大 ...

A security researcher discovered a nasty flaw in Google’s Antigravity tool, the latest example of companies rushing out AI tools vulnerable to hacking. https://t.co/Q9QId52oSQIllustration: Macy Sinreich for Forbes; Photos: HappyPhoton, NurPhoto via Getty Images https://t.co/bVngrh3Ond ...